Discovering synergies between ML, computing and physics.
Cambridge, MA
Joined January 2021
- Tweets15
- Following60
- Followers455
- Likes12
Dmitrii Kochkov retweeted
NeuralGCM was a huge team effort, and I would especially like to acknowledge the core team who has worked on the project over the past three years: @dkochkov1, @janniyuval, @langmore, @pnorgaard314, @singledispatch
Dmitrii Kochkov retweeted
New open source release from my team at Google: Dinosaur, a differentiable dynamical core for global atmospheric modeling, written in JAX: github.com/google-research/d…
Dinosaur is a core component of NeuralGCM and we hope it is useful for the weather/climate research community.
Dmitrii Kochkov retweeted
We have a paper in ICLR! The title is “Learned Coarse Models for Efficient Turbulence Simulation.” We wanted to see if we could train general-purpose ML models to predict turbulent dynamics accurately at low spatial and temporal resolutions (1/n).
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Our work "Learning ground states of quantum Hamiltonians with graph networks" is out!
arxiv.org/abs/2110.06390
TLDR: we do Variational Monte Carlo on Heisenberg Hamiltonians using graph-nets as variational ansatz.
w/
@spectralhippo
Alvaro Sanchez
@PeterWBattaglia
@bkclark4
Dmitrii Kochkov retweeted
Can machine learning improve physics constrained optimization tasks, like those at the heart of numerical weather forecasting?
Happy to share some our work on "Variational Data Assimilation with a Learned Inverse Observation Operator", tomorrow/today at ICML.
🧵
Dmitrii Kochkov retweeted
Come here our selected contributed talk on simulating turbulence with deep learning at #SimDL (simdl.github.io/) today
Led by @neuro_kim, Alvaro Sanchez, @DrumBushField, Dmitrii Kochkov, @MilesCranmer, @spectralhippo, Jonathan Goodwin, Elaine Cui and @PeterWBattaglia
Dmitrii Kochkov retweeted
The paper “Learning Mesh-Based Simulation with Graph Networks” shows how to effectively learn physics simulation on irregular meshes. The model can simulate vastly different systems with significant speedups over the ground truth solver: dpmd.ai/2PubkYS #ICLR2021 (3/3)
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Excited to share "Machine learning accelerated computational fluid dynamics"
arxiv.org/pdf/2102.01010.pdf
We use ML inside a CFD simulator to advance the accuracy/speed Pareto frontier
with/
Jamie A. Smith
Ayya Alieva
Qing Wang
Michael P. Brenner
@shoyer
